This report analyzes the emergency room operations at Taif Children’s Hospital for the month of September 2024. The focus of this month’s report is on understanding peak hours of patient visits, volume, average wait times, triage levels and operational metrics. Recommendations will be provided based on these findings to help enhance patient care and operational efficiency.
Author: Joan Jimenez
Date: October 19, 2024
Introduction
This comprehensive report examines emergency department (ER) operations, focusing on critical key performance indicators (KPIs) that reflect the efficiency and effectiveness of patient care from triage to treatment.It analyzes patient visit metrics to measure overall demand and examines the time from door to doctor, providing insights into initial response times. The report also looks into the duration from doctor to decision, highlighting the speed at which healthcare providers can evaluate and decide on necessary interventions. For more information visit Emergency Room (ER)
Btw, you can learn how to master Quarto thanks to my online course: Productive R Workflow
This document aims at showcasing how versatile the lumo format is. With a few tweaks, I made it fit the brand of a specific company.
Patients Visits
Analyze patterns in patient visits over time to identify peak hours, seasonal variations, and the overall demand for emergency services.By identifying trends and patterns in patient visits, we aim to optimize the quality of care and improve operational effectiveness.
Key Metrics:
Total Patient Visits: 12,500
Average Daily Visits: 139
Peak Hour: 5 PM – 8 PM
Percentage of Non-Urgent Visits: 30%
Percentage of Urgent Visits: 70%
Average Wait Time (Door to Doctor): 20 minutes
Notice that the code chunks are folded by default. You can adjust this behavior with the code-fold option in the document’s YAML header.
By the way, you should open the code chunk that is folded above. ⬆️ There is a little stunning surprise on its right hand side.
This is a good old scatterplot of the iris dataset. If you want to learn more about how to make graphs in R, visit my R graph gallery!
You can also make a boxplot, but please do not hide the underlying distribution! This is widely explain in my data-to-viz.com project in case you’re not convinced 😋. Check the next tab to get convinced!
See what’s happening now that the individual data points are visible! Would be a shame to miss this.
Code
viewof selectedFile = Inputs.select( [ { value:"24clocktime.html",label:"24-Hour Clock Time" }, { value:"ctas_month_waffle.html",label:"CTAS Month Waffle" } ], {label:"Select HTML file",value:"24clocktime.html",format: x => x.label })// Base path for HTML filesbasePath ="interactive/"// Construct full pathfullPath = basePath + selectedFile.value// Create and return the iframe elementhtml`<iframe src="${fullPath}" width="100%" height="500px" style="border:none;"></iframe>`
ER Time Series
I strongly advise to create a dataviz theme that fits your company’s brand. Here is one:
It is very easy to insert an interactive table in your document thanks to the DT package. The output allows to filter rows, search for something and sort using a specific columns!
Install the library with install.packages("DT"). Then, just pass a dataframe to the datatable() function to get a stunning interactive output!
Code
library(DT)data(iris)# Make a tabledatatable(iris, filter ="top")
I’ve added a bit of CSS to make the font smaller on DT tables by the way 🙂
A grey section
It’s always good to have a grey section. Makes the document breath a bit. I’ve added a little utility class in Lumo to make sure you can make sections like this very easily. Check the documentation!
Let’s use this space to render a little equation:
\[
x = \frac{-b \pm \sqrt{b^2 - 4ac}}{2a}
\]
Quarto Tricks
Lumo is a great format, but to get the most out of it, you’ll need to master a few Quarto tricks!
The good news? I’ve compiled my 30+ favorite tips in one place. Just spend 30 minutes, and you’ll be up to speed with all of them!
For example, if you’re creating multiple plots, each highlighting different aspects of your dataset, consider using pills to organize them!
This is a good old scatterplot of the iris dataset. If you want to learn more about how to make graphs in R, visit my R graph gallery!
You can also make a boxplot, but please do not hide the underlying distribution! This is widely explain in my data-to-viz.com project in case you’re not convinced 😋. Check the next tab to get convinced!
Code
# create a dataset
See what’s happening now that the individual data points are visible! Would be a shame to miss this.
